In acca3003/ngboostR: R Wrapper for NGBoost python module

#Install last ve
#library(devtools)
#install_github("acca3003/ngboostR")

library(ngboostR) # R implementation for NGBoost
library(Metrics) # Métrics
library(mlbench) # Breast Cancer dataset
library(caret)


data("BreastCancer")
set.seed(999)
BreastCancer <- na.omit(BreastCancer)
trainIndex <- createDataPartition(BreastCancer$Class, p = .8, 
                                  list = FALSE, 
                                  times = 1)

X_train <- BreastCancer[trainIndex,2:10]
Y_train <- BreastCancer[trainIndex,11]
Y_train <- as.integer(as.integer(Y_train)-1)

X_val <- BreastCancer[-trainIndex,2:10]
Y_val <- BreastCancer[-trainIndex,11]
Y_val <- as.integer(as.integer(Y_val)-1)


# Create the regressor object

class_ngboost <- create_classifier(Base=DecisionTreeRegressor(),
                              Dist=Bernoulli(),
                              col_sample=1.0,
                              learning_rate=0.01, 
                              minibatch_frac=1.0, 
                              n_estimators=as.integer(500),
                              natural_gradient=TRUE,
                              random_state=NULL, 
                              tol=0.0001,
                              verbose=TRUE, 
                              verbose_eval=as.integer(100))
# Train with the boston data
fit_classifier(class_ngboost, X_train, Y_train, X_val, Y_val)


# Predict the price
predictions <- predict_classifier(class_ngboost, X_val)
Metrics::accuracy(Y_val,predictions)

# Predict the price
predictions_prob <- predict_classifier_prob(class_ngboost, X_val)
predictions_prob

# Predict the price as a distribution
predictions_dist <- predict_classifier_dist(class_ngboost, X_val)
predictions_dist